论文标题
翻译人员与流离失所者的准确匹配以克服语言障碍
Accurate and Scalable Matching of Translators to Displaced Persons for Overcoming Language Barriers
论文作者
论文摘要
由于人道主义危机，发展中国家的居民容易受到流离失所的影响。在这种危机期间，语言障碍阻碍了援助人员为流离失所者提供服务。为了建立弹性，此类服务必须灵活且强大，可对许多可能的语言。 \ textit {tarjimly}旨在通过提供一个能够与需要翻译的流离失所者或援助工人相匹配的平台来克服障碍。但是，Tarjimly的大量翻译人员面临着根据请求选择正确的翻译器的挑战。在本文中，我们描述了一个机器学习系统，该系统与翻译人员的请求匹配向志愿者大规模的请求。我们证明，在易于计算的功能上运行的简单逻辑回归可以准确预测和对翻译器的响应。在部署中，这种轻巧的系统与中位响应时间为59秒，匹配82 \％的请求，允许援助人员加速其支持流离失所者的服务。
Residents of developing countries are disproportionately susceptible to displacement as a result of humanitarian crises. During such crises, language barriers impede aid workers in providing services to those displaced. To build resilience, such services must be flexible and robust to a host of possible languages. \textit{Tarjimly} aims to overcome the barriers by providing a platform capable of matching bilingual volunteers to displaced persons or aid workers in need of translating. However, Tarjimly's large pool of translators comes with the challenge of selecting the right translator per request. In this paper, we describe a machine learning system that matches translator requests to volunteers at scale. We demonstrate that a simple logistic regression, operating on easily computable features, can accurately predict and rank translator response. In deployment, this lightweight system matches 82\% of requests with a median response time of 59 seconds, allowing aid workers to accelerate their services supporting displaced persons.
